Ruth Misener
Professor in Computational Optimisation
Develops mixed-integer nonlinear and Bayesian optimisation methods, including optimisation over trained neural networks and tree ensembles, with open-source software.
Ruth Misener's lab at Imperial works on probabilistic & causal inference. 18 papers since 2023, 6 at top venues, mostly NeurIPS and ICML. 1 PhD student. 13 alumni. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 33%
- PhD students
- 1
- MS students
- 0
- alumni listed
- 13
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 7 papers2025 · T1 3
- 4 papers2024 · T1 2
- 7 papers2023 · T1 1
T1 venuesOtherVenues: NeurIPS, ICML, AISTATS
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference6 papers
- optimisation1 paper
- trained1 paper
- open-source0 papers
- nonlinear0 papers
Paper list
From the lab homepage.
20257 papers
- ICML (T1)BARK: A fully Bayesian tree kernel for black-box optimization (opens in a new tab)
- Chemical Society ReviewsCross-disciplinary perspectives on the potential for artificial intelligence across chemistry (opens in a new tab)
- Proceedings of the Genetic and Evolutionary Computation Conference CompanionImproving Deep Optimisation for the Multi-dimensional Knapsack Problem using Elastic Weight Consolidation (opens in a new tab)
- Computers & Chemical EngineeringLimeade: Let integer molecular encoding aid (opens in a new tab)
- NeurIPS (T1)The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning (opens in a new tab)
- Biotechnology and BioengineeringTransfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays (opens in a new tab)
- AISTATS (T1)Weighted-Sum Gaussian Process Latent Variable Models (opens in a new tab)
20244 papers
- Computers & Chemical EngineeringAugmenting optimization-based molecular design with graph neural networks (opens in a new tab)
- Optimization LettersDependence in constrained Bayesian optimization: When do we need it and how does it help? (opens in a new tab)
- NeurIPS (T1)Transition Constrained Bayesian Optimization via Markov Decision Processes (opens in a new tab)
- ICML (T1)Verifying message-passing neural networks via topology-based bounds tightening (opens in a new tab)
20237 papers
- Industrial & Engineering Chemistry ResearchAn Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics (opens in a new tab)
- Computers & Chemical EngineeringCombining multi-fidelity modelling and asynchronous batch Bayesian Optimization (opens in a new tab)
- Computers & Chemical EngineeringData augmentation driven by optimization for membrane separation process synthesis (opens in a new tab)
- Computers & Chemical EngineeringFormulating data-driven surrogate models for process optimization (opens in a new tab)
- NeurIPS (T1)Optimizing over trained GNNs via symmetry breaking (opens in a new tab)
- Optimization & EngineeringPartial Lasserre relaxation for sparse Max-Cut (opens in a new tab)
- AIChE JournalProbabilistic predictions for partial least squares using bootstrap (opens in a new tab)
Where alumni went13 listed
Apply to Imperial2027 (Apr/Oct)
Computing PhD
- Round
- Round 2 (1 Dec 2026)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Confirmed on the program page.
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- Papers
- The lab homepage
- Members
- Lab homepage
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- Lab homepage
Tiers are venue classifications, not lab rankings.
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